summary.gssanova {gss} | R Documentation |
Calculate various summaries of smoothing spline ANOVA fits with non Gaussian responses.
summary[.gssanova](obj, diagnostics=FALSE)
obj |
Object of class "gssanova" . |
diagnostics |
Flag indicating if diagnostics are required. |
Similar to the iterated weighted least squares fitting of
glm
, penalized likelihood regression fit can be calculated
through iterated penalized weighted least squares.
The diagnostics are based on the "pseudo" Gaussian response model behind the weighted least squares problem at convergence.
summary.gssanova
returns a list object of class
"summary.gssanova"
consisting of the following components.
The entries pi
, kappa
, cosines
, and
roughness
are only calculated if diagnostics=TRUE
.
call |
Fitting call. |
family |
Error distribution. |
method |
Method for smoothing parameter selection. |
dispersion |
Assumed or estimated dispersion parameter. |
iter |
Number of performance-oriented iterations performed. |
fitted |
Fitted values on the scale of the link. |
residuals |
Working residuals on the link scale. |
rss |
Residual sum of squares. |
dev.resid |
Deviance residuals. |
deviance |
Deviance of the fit. |
dev.null |
Deviance of the null model. |
penalty |
Roughness penalty associated with the fit. |
pi |
"Percentage decomposition" of "explained variance" into model terms. |
kappa |
Concurvity diagnostics for model terms. Virtually the square roots of variance inflation factors of a retrospective linear model. |
cosines |
Cosine diagnostics for practical significance of model terms. |
roughness |
Percentage decomposition of the roughness penalty
penalty into model terms. |
Chong Gu, chong@stat.purdue.edu
Fitting function gssanova
and methods
predict.ssanova
, fitted.gssanova
.